Soamtic_mutation
Subinoy Biswas
9/23/2019
Taking publicly available dataset from Esophageal carcinoma, acute myeloid leukemia and breast invasive carcinoma the following analysis has been done. I try to include as much results possible as I can.
## -Reading
## -Validating
## -Silent variants: 475
## -Summarizing
## -Processing clinical data
## -Finished in 0.752s elapsed (1.067s cpu)## An object of class MAF
## ID summary Mean Median
## 1: NCBI_Build 37 NA NA
## 2: Center genome.wustl.edu NA NA
## 3: Samples 193 NA NA
## 4: nGenes 1241 NA NA
## 5: Frame_Shift_Del 52 0.271 0
## 6: Frame_Shift_Ins 91 0.474 0
## 7: In_Frame_Del 10 0.052 0
## 8: In_Frame_Ins 42 0.219 0
## 9: Missense_Mutation 1342 6.990 7
## 10: Nonsense_Mutation 103 0.536 0
## 11: Splice_Site 92 0.479 0
## 12: total 1732 9.021 9summary of the maf file, which displays number of variants in each sample as a stacked barplot and variant types as a boxplot summarized by Variant_Classification
#### This is oncoplots, also known as waterfall plots.
#oncoplot for top ten mutated genes.
oncoplot(maf = laml, top = 10)
Mutations in each sample
#### Classifies SNPs into Transitions and Transversions and returns a list of summarized tables in various ways
boxplot showing overall distribution of six different conversions and as a stacked barplot showing fraction of conversions in each sample.
Lollipop plots are simple and most effective way showing mutation spots on protein structure. Many oncogenes have a preferential sites which are mutated more often than any other locus. These spots are considered to be mutational hot-spots and lollipop plots can be used to display them along with rest of the mutations.
## HGNC refseq.ID protein.ID aa.length
## 1: DNMT3A NM_175629 NP_783328 912
## 2: DNMT3A NM_022552 NP_072046 912
## 3: DNMT3A NM_153759 NP_715640 723
Cancer genomes, especially solid tumors are characterized by genomic loci with localized hyper-mutations 5. Such hyper mutated genomic regions can be visualized by plotting inter variant distance on a linear genomic scale. These plots generally called rainfall plots
## Chromosome Start_Position End_Position nMuts Avg_intermutation_dist
## 1: 8 98129391 98133560 6 833.8000
## 2: 8 98398603 98403536 8 704.7143
## 3: 8 98453111 98456466 8 479.2857
## 4: 8 124090506 124096810 21 315.2000
## 5: 12 97437934 97439705 6 354.2000
## 6: 17 29332130 29336153 7 670.5000
## Size Tumor_Sample_Barcode C>G C>T
## 1: 4169 TCGA-A8-A08B 4 2
## 2: 4933 TCGA-A8-A08B 1 7
## 3: 3355 TCGA-A8-A08B NA 8
## 4: 6304 TCGA-A8-A08B 1 20
## 5: 1771 TCGA-A8-A08B 3 3
## 6: 4023 TCGA-A8-A08B 4 3
Genome plots
## An object of class GISTIC
## ID summary
## 1: Samples 191
## 2: nGenes 2622
## 3: cytoBands 16
## 4: Amp 388
## 5: Del 26481
## 6: total 26869
Bubble plot
Oncoplot sorted according to FAB classification
## NULL
Many disease causing genes in cancer are co-occurring or show strong exclusiveness in their mutation pattern. Such mutually exclusive or co-occurring set of genes were detected by pair-wise Fisher’s Exact test
## Checking for Gene sets
## ------------------
## genes: 5
## geneset size: 3
## 10 combinations## $pairs
## gene1 gene2 pValue oddsRatio 00 11 01 10 Event
## 1: ASXL1 RUNX1 0.0001541586 55.215541 176 4 12 1 Co_Occurence
## 2: IDH2 RUNX1 0.0002809928 9.590877 164 7 9 13 Co_Occurence
## 3: IDH2 ASXL1 0.0004030636 41.077327 172 4 1 16 Co_Occurence
## 4: FLT3 NPM1 0.0009929836 3.763161 125 17 16 35 Co_Occurence
## 5: SMC3 DNMT3A 0.0010451985 20.177713 144 6 42 1 Co_Occurence
## 6: DNMT3A NPM1 0.0014582861 3.733141 128 16 17 32 Co_Occurence
## 7: DNMT3A IDH1 0.0033807043 4.462201 137 10 8 38 Co_Occurence
## 8: ASXL1 TTN 0.0077607658 28.459418 184 2 4 3 Co_Occurence
## 9: PHF6 RUNX1 0.0081059811 12.967042 174 3 13 3 Co_Occurence
## 10: TTN RUNX1 0.0081059811 12.967042 174 3 13 3 Co_Occurence
## 11: FLT3 TP53 0.0125113481 0.000000 126 NA 15 52 Mutually_Exclusive
## 12: STAG2 PTPN11 0.0263964643 12.391225 180 2 7 4 Co_Occurence
## 13: IDH2 NPM1 0.0277733049 0.000000 140 NA 33 20 Mutually_Exclusive
## 14: IDH2 KRAS 0.0382620610 5.832674 168 3 5 17 Co_Occurence
## 15: WT1 PHF6 0.0463612252 8.623360 177 2 4 10 Co_Occurence
## 16: NPM1 PTPN11 0.0479288542 4.230142 155 4 5 29 Co_Occurence
## 17: IDH2 PLCE1 0.0540565743 9.280043 171 2 2 18 Co_Occurence
## 18: DNMT3A FLT3 0.0630630121 1.951476 111 18 34 30 Co_Occurence
## 19: NPM1 SMC1A 0.0635083207 5.167266 157 3 3 30 Co_Occurence
## 20: CEBPA NRAS 0.0678045968 4.149259 168 3 12 10 Co_Occurence
## 21: RUNX1 FLT3 0.0740850163 0.165692 126 1 51 15 Mutually_Exclusive
## 22: EZH2 FAM5C 0.0761095136 21.701827 186 1 4 2 Co_Occurence
## 23: TP53 NPM1 0.0785739379 0.000000 145 NA 33 15 Mutually_Exclusive
## 24: NPM1 RUNX1 0.0787933378 0.000000 144 NA 16 33 Mutually_Exclusive
## 25: RUNX1 STAG2 0.0795738073 6.066792 173 2 4 14 Co_Occurence
## 26: TET2 STAG2 0.0888875052 5.638415 172 2 4 15 Co_Occurence
## 27: IDH1 NPM1 0.0914621351 2.722302 148 6 27 12 Co_Occurence
## 28: RAD21 DNMT3A 0.0993846041 4.719039 143 3 45 2 Co_Occurence
## gene1 gene2 pValue oddsRatio 00 11 01 10 Event
##
## $gene_sets
## Empty data.table (0 rows and 2 cols): gene_set,pvalue
Detecting cancer driver genes based on positional clustering
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Adding and summarizing pfam domains
## Warning in pfamDomains(maf = laml, AACol = "Protein_Change", top = 10):
## Removed 50 mutations for which AA position was not available
Pan-Cancer comparison
## gene pancan q nMut log_q_pancan log_q
## 1: CEBPA 1.000 3.500301e-12 13 0.00000000 11.455895
## 2: EZH2 1.000 7.463546e-05 3 0.00000000 4.127055
## 3: GIGYF2 1.000 6.378338e-03 2 0.00000000 2.195292
## 4: KIT 0.509 1.137517e-05 8 0.29328222 4.944042
## 5: PHF6 0.783 6.457555e-09 6 0.10623824 8.189932
## 6: PTPN11 0.286 7.664584e-03 9 0.54363397 2.115511
## 7: RAD21 0.929 1.137517e-05 5 0.03198429 4.944042
## 8: SMC1A 0.801 2.961696e-03 6 0.09636748 2.528460
## 9: TET2 0.907 2.281625e-13 17 0.04239271 12.641756
## 10: WT1 1.000 2.281625e-13 12 0.00000000 12.641756
Survival analysis
## DNMT3A
## 48
## Group medianTime N
## 1: Mutant 243 48
## 2: WT 366 145
## -Reading
## -Validating
## --Non MAF specific values in Variant_Classification column:
## ITD
## -Silent variants: 45
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 0.085s elapsed (0.153s cpu)## -Reading
## -Validating
## --Non MAF specific values in Variant_Classification column:
## ITD
## -Silent variants: 19
## -Summarizing
## -Processing clinical data
## --Missing clinical data
## -Finished in 0.073s elapsed (0.127s cpu)## $results
## Hugo_Symbol Primary Relapse pval or ci.up
## 1: PML 1 11 1.529935e-05 0.03537381 0.2552937
## 2: RARA 0 7 2.574810e-04 0.00000000 0.3006159
## 3: RUNX1 1 5 1.310500e-02 0.08740567 0.8076265
## 4: FLT3 26 4 1.812779e-02 3.56086275 14.7701728
## 5: ARID1B 5 8 2.758396e-02 0.26480490 0.9698686
## 6: WT1 20 14 2.229087e-01 0.60619329 1.4223101
## 7: KRAS 6 1 4.334067e-01 2.88486293 135.5393108
## 8: NRAS 15 4 4.353567e-01 1.85209500 8.0373994
## 9: ARID1A 7 4 7.457274e-01 0.80869223 3.9297309
## ci.low adjPval
## 1: 0.000806034 0.0001376942
## 2: 0.000000000 0.0011586643
## 3: 0.001813280 0.0393149868
## 4: 1.149009169 0.0407875250
## 5: 0.064804160 0.0496511201
## 6: 0.263440988 0.3343630535
## 7: 0.337679367 0.4897762916
## 8: 0.553883512 0.4897762916
## 9: 0.195710173 0.7457273717
##
## $SampleSummary
## Cohort SampleSize
## 1: Primary 124
## 2: Relapse 58
## HGNC refseq.ID protein.ID aa.length
## 1: PML NM_033238 NP_150241 882
## 2: PML NM_002675 NP_002666 633
## 3: PML NM_033249 NP_150252 585
## 4: PML NM_033247 NP_150250 435
## 5: PML NM_033239 NP_150242 829
## 6: PML NM_033250 NP_150253 781
## 7: PML NM_033240 NP_150243 611
## 8: PML NM_033244 NP_150247 560
## 9: PML NM_033246 NP_150249 423
## HGNC refseq.ID protein.ID aa.length
## 1: PML NM_033238 NP_150241 882
## 2: PML NM_002675 NP_002666 633
## 3: PML NM_033249 NP_150252 585
## 4: PML NM_033247 NP_150250 435
## 5: PML NM_033239 NP_150242 829
## 6: PML NM_033250 NP_150253 781
## 7: PML NM_033240 NP_150243 611
## 8: PML NM_033244 NP_150247 560
## 9: PML NM_033246 NP_150249 423
##
## M0 M1 M2 M3 M4 M5 M6 M7
## 19 44 44 21 39 19 3 3
## Number of claimed drugs for given genes:
## Gene N
## 1: DNMT3A 7## Pathway N n_affected_genes fraction_affected
## 1: RTK-RAS 85 18 0.21176471
## 2: Hippo 38 7 0.18421053
## 3: NOTCH 71 6 0.08450704
## 4: MYC 13 3 0.23076923
## 5: WNT 68 3 0.04411765
## 6: TP53 6 2 0.33333333
## 7: NRF2 3 1 0.33333333
## 8: PI3K 29 1 0.03448276
## 9: Cell_Cycle 15 0 0.00000000
## 10: TGF-Beta 7 0 0.00000000
